Small, noncoding RNA genes pervade bacteria. Our understanding of these noncoding genes has increased dramatically in recent years, thanks, in part, to advances in high-throughput sequencing technology. High-throughput sequencing technology enables, among other things, experiments that produce massive amounts of data about RNA transcripts in bacteria. However, processing the large resulting data sets from high-throughput sequencing experiments can be a bottleneck in biological and medical research studies, partly because existing methods are insufficient for analyzing these data sets from bacteria. This project aims to develop a computational system for managing and analyzing large sets of data from bacterial high-throughput sequencing experiments. As part of this computational system, new algorithms will be developed for determining a map of all RNA transcripts evinced by high-throughput sequencing data for a bacterial species. Further, a public database will be created to store and manage information about the growing number of small RNA genes characterized in bacteria. Finally, since many small RNA genes in bacteria act as regulators of other RNAs, computational methods will be developed to identify the interactions between these noncoding genes and their RNA targets. The methods developed will be applied and evaluated in several different bacterial systems.